Generating Representative Driving Cycles

Abstract

The transportation industry is rapidly evolving. Customer expectations and environmental sustainability demands are quickly shifting, making performance optimisation of Heavy-Duty Vehicles (HDVs) increasingly critical. An essential aspect of the optimisation process is having Driving Cycles (DCs) that are representative of real-world driving patterns for accurate vehicle verification and validation.

In this thesis, different advanced data analytics methods were implemented and evaluated on their ability to generate DCs representative of real-driving patterns of HDVs. The implemented methods belonged to three main categories explaining the general technique of how DCs are constructed: Speed Acceleration State (SAS) methods, Micro-Trip (MT) methods, and Kinematic Segment (KS) methods.

To quantitatively assess method effectiveness, two main metrics were used: Characteristic Parameters (CPs), and Speed Acceleration Probability Distribution (SAPD). The CPs describe different statistical characteristics of driving behaviour which were compared between generated cycles and the operational data. Additionally, a comparison between generated cycles and the Vehicle Energy Consumption Calculation Tool (VECTO) was also made to provide a performance baseline. CPs were evaluated using Relative Difference (RD), while SAPD was evaluated with RD and Earth Mover’s Distance (EMD). EMD measures distribution dissimilarities, and its addition to the evaluation offers a more reliable SAPD assessment than what has been done in previous research.

The results showed that all implemented methods outperform the VECTO baseline both in terms of CP and SAPD representativeness, highlighting the need for fine-tuned cycles. The SAS and MT methods demonstrated superior performance compared to the KS methods. Especially in terms of SAPD representativeness and computational efficiency. While the KS methods showed significant limitations, the SAS and MT methods achieved highly promising CP and SAPD representations of the operational data. The SAS and MT methods were concluded as viable methods for DC generation and are the methods suggested to continue researching.

The thesis aims to serve as a DC generation framework that future researchers can follow, including detailed descriptions of all necessary methodological steps. The framework details a systematic approach for creating representative DCs adaptable for any driving profile or vehicle type.